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Remote Sensing
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The Recent Progression of Machine Learning in Remote Sensing: Theory and Modelling (Second Edition)

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "AI Remote Sensing".

Deadline for manuscript submissions: 31 July 2026 | Viewed by 1187

Special Issue Editors

Dr. Rong Wang

E-Mail Website
Guest Editor
School of Artificial Intelligence, Optics and Electronics (iOPEN), The Key Laboratory of Intelligent Interaction and Applications (Ministry of Industry and Information Technology), Northwestern Polytechnical University, Xi’an 710072, China
Interests: machine learning; remote sensing; image processing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Lefei Zhang

grade E-Mail Website
Guest Editor
School of Computer Science, Wuhan University, Wuhan 430072, China
Interests: pattern recognition; machine learning; image processing; remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Machine learning has emerged as a powerful tool in remote sensing, enabling the analysis and interpretation of large-scale and complex datasets with remarkable accuracy and efficiency. By leveraging statistical theory, learning theory, and neural networks, machine learning methods can automatically learn patterns and relationships within remote sensing data, uncovering hidden information and aiding in the understanding of various phenomena.

This Special Issue will provide researchers with the opportunity to present theoretical research focusing on modelling techniques of machine learning for remote sensing data analysis. Articles included in this Special Issue may address but are not limited to the following topics in remote sensing imaging:

  1. Image classification;
  2. Image clustering;
  3. Image denoising;
  4. Objective detection/object tracking;
  5. Change detection/anomaly detection;
  6. Machine learning theory for RS data: generalizability, interpretability, etc.

Dr. Rong Wang
Prof. Dr. Lefei Zhang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • machine learning
  • image processing
  • remote sensing
  • data fusion
  • satellite images
  • aerial images

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Further information on MDPI's Special Issue policies can be found here.

Related Special Issue

Published Papers (2 papers)

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Research

25 pages, 8404 KB  
Article
Ladder-Side-Tuning of Visual Foundation Model for City-Scale Individual Tree Detection from High-Resolution Remote Sensing Images
by Chen Huang, Ying Ding, Kun Xiao, Rong Liu and Ying Sun
Remote Sens. 2026, 18(5), 819; https://doi.org/10.3390/rs18050819 - 6 Mar 2026
Viewed by 346
Abstract
Accurate detection of individual trees is essential for urban forest management and ecological assessment, yet remains challenging due to the heterogeneous backgrounds, variable sizes of tree crowns, and significant variations across urban scenarios. To address these issues, we propose Tree-SAM, a city-scale individual [...] Read more.
Accurate detection of individual trees is essential for urban forest management and ecological assessment, yet remains challenging due to the heterogeneous backgrounds, variable sizes of tree crowns, and significant variations across urban scenarios. To address these issues, we propose Tree-SAM, a city-scale individual tree detection architecture built upon the visual foundation model Segment Anything Model (SAM) and equipped with three task-specific modules, i.e., Cross-Correlation Feature Backbone (CCFB), Hierarchical Instance Aggregation Neck (HIAN), and Context-Aware Adaptation Head (CAAH). These modules synergistically fuse general semantics with fine-grained structural cues, enable multi-scale feature aggregation, and adaptively refine predictions based on specific scene contexts. On the GZ-Tree Crown dataset, Tree-SAM achieves F1-scores of 0.762, 0.732, and 0.830, with corresponding AP@50 values of 0.478, 0.454, and 0.526 in the forest, mixed, and urban scenarios, respectively, consistently ranking first across all scenes and demonstrating strong adaptability to diverse intra-city landscapes. Additional evaluations on the BAMFORESTS dataset and the SZ-Dataset further confirm its robustness across varied geographic contexts. Tree-SAM provides a reliable, automated framework for large-scale urban tree mapping, providing reliable data support for urban forest management, carbon stock estimation, and ecological assessment. Full article
(This article belongs to the Special Issue The Recent Progression of Machine Learning in Remote Sensing: Theory and Modelling (Second Edition))
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30 pages, 14511 KB  
Article
Rural Settlement Segmentation in Large-Scale Remote Sensing Imagery Using MSF-AL Auto-Labeling and the SELPFormer Model
by Qian Zhou, Yongqi Sun, Yanjun Tian, Qiqi Deng, Shireli Erkin and Yongnian Gao
Remote Sens. 2026, 18(4), 579; https://doi.org/10.3390/rs18040579 - 12 Feb 2026
Viewed by 450
Abstract
Accurate delineation of rural settlements at large spatial extents is fundamental to territorial spatial governance, rural revitalization, and the improvement of human living environments. However, in medium-resolution remote sensing imagery, rural settlement patches are typically small, morphologically complex, and easily confused with other [...] Read more.
Accurate delineation of rural settlements at large spatial extents is fundamental to territorial spatial governance, rural revitalization, and the improvement of human living environments. However, in medium-resolution remote sensing imagery, rural settlement patches are typically small, morphologically complex, and easily confused with other impervious surfaces. As a result, existing products still fall short in characterizing these features. Here, we propose a lightweight Transformer-based semantic segmentation model, SELPFormer, and develop a multi-source fusion automatic labeling pipeline that integrates Global Impervious Surface Dynamics dataset, OpenStreetMap spatial priors, and nighttime lights constraints. Built upon SegFormer as the backbone, SELPFormer introduces a lightweight pyramid pooling module at the deepest feature level to aggregate multi-scale global context and embeds an SCSE channel–spatial attention mechanism into deep features to suppress background interference. In addition, it incorporates an efficient local attention module into multi-scale lateral connections to enhance boundary and texture representations, thereby jointly improving small-object recognition and fine boundary preservation. We evaluate the proposed method using Landsat multispectral imagery covering five provinces on the North China Plain. SELPFormer achieves IoU = 74.23%, mIoU = 86.43%, F1 = 85.21%, OA = 98.69%, and Kappa = 0.8452 under a unified training and evaluation protocol, yielding IoU gains of +1.44, +3.98, and +12.35 percentage points over SegFormer, U-Net, and DeepLabV3+, respectively. SELPFormer has 15.44 M parameters and attains a parameter efficiency of 3.93% IoU per million parameters and an ROC-AUC of 0.993, indicating strong threshold-independent discriminative capability. These results indicate that the proposed method can effectively extract rural settlements from medium-resolution imagery and provides a generic “global–channel–local” collaborative framework for model design and data construction. Full article
(This article belongs to the Special Issue The Recent Progression of Machine Learning in Remote Sensing: Theory and Modelling (Second Edition))
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